---
title: "MBON PIMCPA"
output:
flexdashboard::flex_dashboard:
theme: lumen
social: menu
source: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(leaflet)
library(ggplot2)
library(plotly)
library(spocc)
library(rinat)
library(mapr)
library(RColorBrewer)
palette(brewer.pal(8, "Set2"))
```
```{r dataread, message=FALSE, warning=FALSE, include=FALSE}
#READ both files metadata and percent covers
setwd(paste0(getwd(),"/DATA"))#set new WD to folder DATA
PIMCPA.cover <- read.csv("percent_covers.csv")
PIMCPA.metadata <- read.csv("metadata.csv")
setwd("..")# original WD
colnames(PIMCPA.cover)[2] <-"Name"
#Merge PIMCPA.metadata and PIMCPA.cover
PIMCPA<- merge(PIMCPA.metadata,PIMCPA.cover, by = "Name", all.x = TRUE)
#Create long type dataframe
library(reshape)
PIMCPA_long = melt(PIMCPA, id.vars = 1:21, measure.vars = 22:ncol(PIMCPA), variable_name = "CATAMI", value_name ="cover", na.rm = T)
#rename columns because the ontop command is not working
colnames(PIMCPA_long)[23] <- "cover"
#Calculate mean, SD, SE for cover data by factors
library(doBy)
Coverdata <- summaryBy(cover ~ CATAMI + strata,data=PIMCPA_long, FUN = function(x) { c(mean = mean(x),SD=sd(x),SE = sqrt(var(x)/length(x)))})
#add year from date info
PIMCPA$year <- lubridate::year(PIMCPA$Date)
photo_bydate = as.data.frame(table(PIMCPA$year,PIMCPA$site,PIMCPA$strata))
colnames(photo_bydate)=c("Fecha","Sitio","Estrato","n fotocuadrantes")
```
Column1 {.tabset .tabset-fade}
-------
### Mapa Fotoquadrantes
```{r map, message=FALSE, warning=FALSE}
library(leaflet)
leaflet() %>%
addMiniMap(toggleDisplay = T) %>%
addProviderTiles(providers$Esri.WorldImagery) %>%
addCircleMarkers(data = PIMCPA, ~Longitude, ~Latitude,weight = 0.5,col = 'green', fillColor = 'green',radius = 4, fillOpacity = 0.5, stroke = T, label =PIMCPA$Name,group ='Fotocuadrantes')%>%
addLayersControl(overlayGroups = c("Fotocuadrantes"),options = layersControlOptions(collapsed = FALSE),position = 'topright')
```
### Phylum
```{r donut}
library(plotly)
##numbers of observations by phylum
p = Coverdata %>% plot_ly(labels = ~CATAMI, values=~cover.mean) %>%
add_pie(hole=0.6) %>%
layout(title = ~paste0("Porcentaje de cobertura promedio por categoria "))
plotly::config(p,displayModeBar = F)
```
Column3{data-width=200}
-------
### Fotoquadrantes por fecha y sitio
```{r}
knitr::kable(as.data.frame(table(PIMCPA$year,PIMCPA$site,PIMCPA$strata)),col.names = c("Año","Sitio","Estrato","n"))
```